Kinetics and Optimization of Metal Leaching from Heat-Resistant Nickel Alloy Solid Wastes.

Recycling waste from the production and consumption of heat-resistant alloys to return them to production is an urgent task due to the high cost of the components contained in these alloys. The kinetics and conditions of the acid leaching process of the grinding waste of a heat-resistant nickel alloy are studied depending on the composition of the acid solution (H2SO4, HCl, HNO3, and their mixtures) at room temperature to boiling point temperature and various acid concentrations (1.5 to 3.0 mol/L), ratios of waste to solution (1:10 to 3:10), fraction sizes (0.04-1 mm), and contact duration (1 to 120 h). The linearization of experimental data by the Gray-Weddington, Gistling-Brownstein, and Kazeev-Erofeev equations showed that the rate of the leaching process was influenced by both the chemical reactions between sulfuric acid and metals included in the grinding waste and the diffusion of reagents through the film of reaction products and undissolved impurities. Optimal conditions for acid dissolution of the grinding waste have been established to obtain the maximum degree of extraction of the main component of the alloy, nickel. The processing of powder particles with a size of less than 0.1 mm should be carried out in a solution of sulfuric acid with a concentration of 3.0 mol/L at a temperature of 100 °C for 6 h with a ratio of solid to liquid phases of 1:10. The reported results are very important for industry personnel to recover metals and for environmentalists to treat the alloy waste.

A Dutch Study of Remarkable Recoveries After Prayer: How to Deal with Uncertainties of Explanation.

This article addresses cases of remarkable recoveries related to healing after prayer. We sought to investigate how people who experienced remarkable recoveries re-construct and give meaning to these experiences, and examine the role that epistemic frameworks available to them, play in this process. Basing ourselves on horizontal epistemology and using grounded theory, we conducted this qualitative empirical research in the Netherlands in 2016-2021. It draws on 14 in-depth interviews. These 14 cases were selected from a group of 27 cases, which were evaluated by a medical assessment team at the Amsterdam University Medical Centre. Each of the participants had experienced a remarkable recovery during or after prayer. The analysis of the interviews, which is based on the grounded theory approach, resulted in three overarching themes, placing possible explanations of the recoveries within (1) the medical discourse, (2) biographical discourse, and (3) a discourse of spiritual and religious transformation. Juxtaposition of these explanatory frameworks provides a way to understand better the transformative experience that underlies remarkable recoveries. Uncertainty regarding an explanation is a component of knowing and can facilitate a dialogue between various domains of knowledge.

The nCOVID-19 and financial stress in the USA: health is wealth.

Since its first report in the USA on 13 January 2020, the novel coronavirus (nCOVID-19) pandemic like in other previous epicentres in India, Brazil, China, Italy, Spain, UK, and France has until now hampered economic activities and financial markets. To offer one of the first empirical insights into the economic/financial effect of the COVID-19 pandemic, especially in the USA, this study utilized the daily frequency data for the period 25 February 2020-30 March 2020. By employing the empirical Markov switching regression approach and the compliments of cointegration techniques, the study establishes a two-state (stable and distressing) financial stress situation resulting from the effects of COVID-19 daily deaths, COVID-19 daily recovery, and the USA' economic policy uncertainty. From the result, it is assertive that daily recovery from COVID-19 eases financial stress, while the reported daily deaths from COVID-19 further hamper financial stress in the country. Moreover, the uncertainty of the USA' economic policy has also cost the Americans more financial stress and other socio-economic challenges. While the cure for COVID-19 remains elusive, as a policy instrument, the USA and similar countries with high severity of COVID-19 causalities may intensify and sustain the concerted efforts targeted at attaining a landmark recovery rate.

The shape of recovery: Implications of past experience for the duration of the COVID-19 recession.

In this paper we seek to make headway on the question of what recovery from Covid-19 recession may look like, focusing on the duration of the recovery - that is, how long it will take to re-attain the levels of output and employment reached at the prior business cycle peak. We start by categorizing all post-1960 recessions in advanced countries and emerging markets into supply-shock, demand-shock and both-shock induced recessions. We measure recovery duration as the number of years required to re-attain pre-recession levels of output or employment. We then rely on the earlier literature on business cycle dynamics to identify candidate variables that can help to account for variations in recovery duration following different kinds of shocks. By asking which of these variables are operative in the Covid-19 recession, we can then draw inferences about the duration of the recovery under different scenarios. A number of our statistical results point in the direction of lengthy recoveries.

Modelling and forecasting of new cases, deaths and recover cases of COVID-19 by using Vector Autoregressive model in Pakistan.

COVID-19 emerged in Wuhan, China in December 2019 has now spread around the world causes damage to human life and economy. Pakistan is also severely effected by COVID-19 with 202,955 confirmed cases and total deaths of 4,118. Vector Autoregressive time series models was used to forecast new daily confirmed cases, deaths and recover cases for ten days. Our forecasted model results show maximum of 5,363/day new cases with 95% confidence interval of 3,013-8,385 on 3rd of July, 167/day deaths with 95% confidence interval of 112-233 and maximum recoveries 4,016/day with 95% confidence interval of 2,182-6,405 in the next 10 days. The findings of this research may help government and other agencies to reshape their strategies according to the forecasted situation. As the data generating process is identified in terms of time series models, then it can be updated with the arrival of new data and provide forecasted scenario in future.

Statistical analysis of forecasting COVID-19 for upcoming month in Pakistan.

In this paper, we have conducted analysis based on data obtained from National Institute of Health (NIH) - Islamabad and produced a forecast of COVID-19 confirmed cases as well as the number of deaths and recoveries in Pakistan using the Auto-Regressive Integrated Moving Average Model (ARIMA). The fitted forecasting models revealed high exponential growth in the number of confirmed cases, deaths and recoveries in Pakistan. Based on our model prediction the number of confirmed cases will be increased by 2.7 times, 95% prediction interval for the number of cases at the end of May 2020 = (5681 to 33079). There could be up to 500 deaths, 95% prediction interval = (168 to 885) and there could be eightfold increase in the number of recoveries, 95% prediction interval = (2391 to 16126). The forecasting results of COVID-19 are alarming for May in Pakistan. The health officials and government should adopt new strategies to control the pandemic from further spread until a proper treatment or vaccine is developed.

Analysis on novel coronavirus (COVID-19) using machine learning methods.

In this paper, we are working on a pandemic of novel coronavirus (COVID-19). COVID-19 is an infectious disease, it creates severe damage in the lungs. COVID-19 causes illness in humans and has killed many people in the entire world. However, this virus is reported as a pandemic by the World Health Organization (WHO) and all countries are trying to control and lockdown all places. The main objective of this work is to solve the five different tasks such as I) Predicting the spread of coronavirus across regions. II) Analyzing the growth rates and the types of mitigation across countries. III) Predicting how the epidemic will end. IV) Analyzing the transmission rate of the virus. V) Correlating the coronavirus and weather conditions. The advantage of doing these tasks to minimize the virus spread by various mitigation, how well the mitigations are working, how many cases have been prevented by this mitigations, an idea about the number of patients that will recover from the infection with old medication, understand how much time will it take to for this pandemic to end, we will be able to understand and analyze how fast or slow the virus is spreading among regions and the infected patient to reduce the spread based clear understanding of the correlation between the spread and weather conditions. In this paper, we propose a novel Support Vector Regression method to analysis five different tasks related to novel coronavirus. In this work, instead of simple regression line we use the supported vectors also to get better classification accuracy. Our approach is evaluated and compared with other well-known regression models on standard available datasets. The promising results demonstrate its superiority in both efficiency and accuracy.

COVID-19: Clinical aspects and therapeutics responses.

COVID-19 has created havoc in the world by causing thousands of demises in a short period of time. Up till now, several attempts have been made for potential therapeutics against SARS-COV2. In this retrospective, single-center study, we extracted data from 122 COVID-19, RT-PCR confirmed patients. who were treated with a new treatment strategy of lianhuaqingwen with Arbidol Hydrochloride. The patients were either asymptomatic or had mild symptoms for COVID-19 disease. Of 122 patients 21 (17.21%) patients developed severe conditions of COVID-19, while total 111 (90.9%) experienced mild symptoms such as fever in 93 (76.22%) patients, cough in 23 (20.17%) and muscle pain were observed in total 8 (7%) patients. Furthermore our newly applied drugs combination (Lianhuaqingwen and Arbidol Hydrochloride) showed therapeutic effects in 5-7 days in patients with mild symptoms with 98% recovery rate. These results indicate that COVID-19 patients with mild symptoms can be treated with Lianhuaqingwen and Arbidol Hydrochloride. However, extensive clinical investigations are required to confirm the effectiveness of these drugs.

Simultaneous Detection of Adenosine Triphosphate and Glucose Based on the Cu-Fenton Reaction.

Both adenosine triphosphate (ATP) and glucose are important to human health, and their abnormal levels are closely related to angiocardiopathy and hypoglycaemia. Therefore, the simultaneous determination of ATP and glucose with a single test mode is highly desirable for disease diagnostics and early recognition. Herein, a new fluorescence on/off switch sensing platform is developed by carbon nanodots (CNDs) to detect ATP and glucose simultaneously. The fluorescence of CNDs can be quenched by Cu2+ and hydrogen peroxide (H2O2), due to the formation of hydroxyl radicals (·OH) produced in the Cu-Fenton reaction. Based on the high affinity of Cu2+ with ATP, the fluorescence of CNDs will recover effectively after adding ATP. Additionally, glucose can be efficiently catalyzed by glucose oxidase (GOx) to generate H2O2, so the platform can also be utilized to analyze glucose. Under optimum conditions, this sensing platform displays excellent sensitivity and the linear ranges are from 0.1 to 7 μM for ATP with a limit of detection (LOD) of 30.2 nM, and from 0.1 to 7 mM for glucose with a LOD 39.8 μM, respectively. Benefiting from the high sensitivity and selectivity, this sensing platform is successfully applied for simultaneous detection of ATP and glucose in human serum samples with satisfactory recoveries.

Recovering from disasters: a study of livelihoods in post-quake villages in northern Pakistan.

The October 2005 earthquake in northern Pakistan severely affected the livelihoods of 1.5 million people. With the destruction of material assets and communications infrastructure, the quake had a devastating impact on people's way of life in this remote mountainous region. This paper explores livelihood revival interventions undertaken during the earthquake response, and considers how differentiated livelihood outcomes were achieved. In addressing this objective the paper examines livelihood rehabilitation schemes in terms of structural aspects, working strategies, key factors, strengths of interventions and the role of human agency in influencing livelihood trajectories of quake-affected communities. Primary data for this study was gathered in northern Pakistan between October 2008 and January 2009. The study identifies structural shortcomings and strengths of the programmes attempting to revive the livelihoods of poor and vulnerable households. It identifies how households in two villages made the most of opportunities to improve their lives and move towards favourable outcomes.

Non-targeted analysis of lipidic extracts by high-resolution mass spectrometry to characterise the chemical exposome: Comparison of four clean-up strategies applied to egg.

Biota samples are used to monitor chemical stressors and their impact on the ecosystem and to describe dietary chemical exposure. These complex matrices require an extraction step followed by clean-up to avoid damaging sensitive analytical instruments based on chromatography coupled to mass spectrometry. While interest for non-targeted analysis (NTA) is increasing, there is no versatile or generic sample preparation for a wide range of contaminants suitable for a diversity of biotic matrices. Among the contaminants' variety, persistent contaminants are mostly hydrophobic (mid- to non-polar) and bio-magnify through the lipidic fraction. During their extraction, lipids are generally co-extracted, which may cause matrix effect during the analysis such as hindering the acquired signal. The aim of this study was to evaluate the efficacy of four clean-up methods to selectively remove lipids from extracts prior to NTA. We evaluated (i) gel permeation chromatography (GPC), (ii) Captiva EMR-lipid cartridge (EMR), (iii) sulphuric acid degradation (H2SO4) and (iv) polydimethyl siloxane (PDMS) for their efficiency to remove lipids from hen egg extracts. Gas and liquid chromatography coupled with high-resolution mass spectrometry fitted with either electron ionisation or electrospray ionisation sources operating in positive and negative modes were used to determine the performances of the clean-up methods. A set of 102 chemicals with a wide range of physico-chemical properties that covers the chemical space of mid- to non-polar contaminants, was used to assess and compare recoveries and matrix effects. Matrix effects, that could hinder the mass spectrometer signal, were lower for extracts cleaned-up with H2SO4 than for the ones cleaned-up with PDMS, EMR and GPC. The recoveries were satisfactory for both GPC and EMR while those determined for PDMS and H2SO4 were low due to poor partitioning and degradation/dissociation of the compounds, respectively. The choice of the clean-up methods, among those assessed, should be a compromise that takes into account the matrix under consideration, the levels and the physico-chemical properties of the contaminants.

Translational Research on Bee Pollen as a Source of Nutrients: A Scoping Review from Bench to Real World.

The emphasis on healthy nutrition is gaining a forefront place in current biomedical sciences. Nutritional deficiencies and imbalances have been widely demonstrated to be involved in the genesis and development of many world-scale public health burdens, such as metabolic and cardiovascular diseases. In recent years, bee pollen is emerging as a scientifically validated candidate, which can help diminish conditions through nutritional interventions. This matrix is being extensively studied, and has proven to be a very rich and well-balanced nutrient pool. In this work, we reviewed the available evidence on the interest in bee pollen as a nutrient source. We mainly focused on bee pollen richness in nutrients and its possible roles in the main pathophysiological processes that are directly linked to nutritional imbalances. This scoping review analyzed scientific works published in the last four years, focusing on the clearest inferences and perspectives to translate cumulated experimental and preclinical evidence into clinically relevant insights. The promising uses of bee pollen for malnutrition, digestive health, metabolic disorders, and other bioactivities which could be helpful to readjust homeostasis (as it is also true in the case of anti-inflammatory or anti-oxidant needs), as well as the benefits on cardiovascular diseases, were identified. The current knowledge gaps were identified, along with the practical challenges that hinder the establishment and fructification of these uses. A complete data collection made with a major range of botanical species allows more robust clinical information.

Assessment and validation of the p-QuEChERS sample preparation methodology for the analysis of >200 veterinary drugs in various animal-based food matrices.

The need remains for veterinary multi-residue methods that reliably quantify and identify veterinary drugs in the various animal-based food matrices. Such a method should not only show good method performance parameters (e.g. recoveries of analytes) but must also be fast and cheap. The proposed method focused on the following points: acceptable analyte trueness (recovery) and precision for a large number (200) of diverse veterinary drugs in the relevant animal-based food matrices (egg, muscle, fatty fish, liver, kidney, and honey). The sample preparation method termed p-QuEChERS uses a salt mixture consisting of potassium phosphates to induce phase separation. The avoidance of conventional QuEChERS salts (e.g. magnesium sulphate) significantly improves recoveries of several critical analytes. Analyte recoveries were further improved by adding a centrifugation and a defatting step before initiating the salt-induced phase separation. This combined clean-up removes a large fraction of the potentially interfering matrix compounds. As a result, matrix effects in the electrospray interface were minimized. These factors were the basis for the obtained good validation data. Two types of high-resolution mass spectrometers coupled to liquid chromatography were compared for analysis. In comparison with conventional QuEChERS, the proposed p-QuEChERS concept improved the recovery of polar analytes such as penicillins, tetracyclines and quinolones. The simplicity of the procedure and the low consumable expenses make the method ideal for the routine control of veterinary drugs in all evaluated animal-based food matrices.

Coronavirus disease 2019 in proportion to population: a historical analysis of Saudi Arabia.

Background: Saudi Arabia is one of the countries seriously affected by coronavirus disease 2019 (COVID-19) worldwide. With a few cases in early March, the daily spread of this disease increased to nearly 5000 at one point in time during the first wave to mid-June 2020. With committed efforts and public health interventions, it has been controlled to nearly 1000 by the end of August 2020 and less than 217 by November 28, 2020; thereafter, reporting declines and small increases. However, by December 2021, a third wave started, lasting for 2 months, during which the infection rate increased rapidly. By April 1, 2022, the number of infected persons in the country was 750,998, with 9047 deaths, 7131 active, and approximately 400 critical cases. This analysis of COVID-19 statistics of the Ministry of Health of Saudi Arabia (March 2020-April 2022) is carried out along with population data to extract patient proportions per 100,000 persons to illustrate the hypothesized social and community impact, which influences families and households. Results: The results showed a high rate of infection and mortality, but with recovery. These rates varied across localities and cities. A few cities with higher population densities are less affected by the spread of the epidemic. However, few localities and upcoming cities/townships were severely affected. These effects are explained as the percentage of the population affected, which exposes the impact on societies, families, and individual members. With concerted efforts, they are brought under control through recovery and adopting mitigation methods. Conclusions: Localities could be classified into four categories based on the proportion of the infected population: rapidly increasing, moderately increasing, declining, and stabilizing. Moreover, differential proportions of the affected population have implications at social and familial levels. Analysis and understanding of these trends, considering the base population, are important for policy building and intervention strategies accounting for grassroots-level demographics, which might serve as a tool to enhance interventions at population and family levels. Strategies for awareness creation and compassionate care are essential to address the psychosocial impact of health emergencies, as proved by the Ministry of Health, Saudi Arabia.

Forecasting COVID-19 situation in Bangladesh.

Forecasting the COVID-19 confirmed cases, deaths, and recoveries demands time to know the severity of the novel coronavirus. This research aims to predict all types of COVID-19 cases (verified people, deaths, and recoveries) from the deadliest 3rd wave data of the COVID-19 pandemic in Bangladesh. We used the official website of the Directorate General of Health Services as our data source. To identify and predict the upcoming trends of the COVID-19 situation of Bangladesh, we fit the Auto-Regressive Integrated Moving Average (ARIMA) model on the data from Mar. 01, 2021 to Jul. 31, 2021. The finding of the ARIMA model (forecast model) reveals that infected, deaths, and recoveries number will have experienced exponential growth in Bangladesh to October 2021. Our model reports that confirmed cases and deaths will escalate by four times, and the recoveries will improve by five times at a later point in October 2021 if the trend of the three scenarios of COVID-19 from March to July lasts. The prediction of the COVID-19 scenario for the next three months is very frightening in Bangladesh, so the strategic planner and field-level personnel need to search for suitable policies and strategies and adopt these for controlling the mass transmission of the virus.

Analytical method development and validation for the quantification of metaldehyde in soil.

Previously published methods for the analysis of metaldehyde were adapted for its reliable quantification in soil extracts. Varied methanol-water extraction solvents were trialed, but the use of pure methanol proved to be the most reliable approach for the scaled down methodology. Analysis of metaldehyde was done using LC-MS. Initially the method had problems with matrix suppression of the signal. The method was therefore further developed to overcome this challenge to avoid the costs and time demands of laborious clean-up protocols. This modification to the method involved use of the BEH Phenyl column instead of the C18 column initially used, and optimization of the gradient flow of the mobile phase. The optimized LC-MS method was validated and used for further research applications. In brief,•We investigated the recovery of metaldehyde from spiked soil samples.•The optimized LC-MS method achieved acceptable metaldehyde recoveries (100-132%, 109% on average) for a range of soil types.•The optimized method was suitable for high through-put analyzes.

Further Insights on the Migration Biology of Monarch Butterflies, Danaus plexippus (Lepidoptera: Nymphalidae) from the Pacific Northwest.

The fall migration of monarch butterflies, Danaus plexippus (L.), in the Pacific Northwest was studied during 2017-2019 by tagging 14,040 captive-reared and 450 wild monarchs. One hundred and twenty-two captive-reared monarchs (0.87%) were recovered at distances averaging 899.9 ± 98.6 km for Washington-released and 630.5 ± 19.9 km for Oregon-released monarchs. The greatest straight-line release to recovery distance was 1392.1 km. A mean travel rate of 20.7 ± 2.2 km/day and maximum travel of 46.1 km/day were recorded. Recovery rates were greater for Oregon-released monarchs (0.92%) than Washington-released (0.34%) or Idaho-released monarchs (0.30%). Most monarchs (106/122) were recovered SSW-S-SSE in California, with 82 at 18 coastal overwintering sites. Two migrants from Oregon were recovered just weeks after release ovipositing in Santa Barbara and Palo Alto, CA. Two migrants released in central Washington recovered up to 360.0 km to the SE, and recoveries from Idaho releases to the S and SE suggests that some Pacific Northwest migrants fly to an alternative overwintering destination. Monarchs released in southern Oregon into smoky, poor quality air appeared to be as successful at reaching overwintering sites and apparently lived just as long as monarchs released into non-smoky, good quality air. Migration and lifespan for monarchs infected with the protozoan parasite, Ophryocystis elektroscirrha (McLaughlin and Myers), appeared to be similar to the migration and survival of uninfected monarchs, although data are limited. Our data improve our understanding of western monarch migration, serving as a basis for further studies and providing information for conservation planning.

Short-term forecasting of daily infections, fatalities and recoveries about COVID-19 in Algeria using statistical models.

BACKGROUND: A viral disease due to a virus called SARS-Cov-2 spreads globally with a total of 34,627,141 infected people and 1,029,815 deaths. Algeria is an African country where 51,690, 1,741 and 36,282 are currently reported as infected, dead and recovered. A multivariate time series model has been used to model these variables and forecast their future scenarios for the next 20 days. RESULTS: The results show that there will be a minimum of 63 and a maximum of 147 new infections in the next 20 days with their corresponding 95% confidence intervals of - 89 to 214 and 108-186, respectively. Deaths' forecast shows that there will be 8 and 12 minimum and maximum numbers of deaths in the upcoming 20 days with their 95% confidence intervals of 1-17 and 4-20, respectively. Minimum and maximum numbers of recovered cases will be 40 and 142 with their corresponding 95% confidence intervals of - 106 to 185 and 44-239, respectively. The total number of infections, fatalities and recoveries in the next 20 days will be 1850, 186 and 1680, respectively. CONCLUSION: The results of this study suggest that the new infections are higher in number than recover cases, and therefore, the number of infected people may increase in future. This study can provide valuable information for policy makers including health and education departments.

Global Healthcare Resource Efficiency in the Management of COVID-19 Death and Infection Prevalence Rates.

The scale of impact of the COVID-19 pandemic on society and the economy globally provides a strong incentive to thoroughly analyze the efficiency of healthcare systems in dealing with the current pandemic and to obtain lessons to prepare healthcare systems to be better prepared for future pandemics. In the absence of a proven vaccine or cure, non-pharmaceutical interventions including social distancing, testing and contact tracing, isolation, and wearing of masks are essential in the fight against the worldwide COVID-19 pandemic. We use data envelopment analysis and data compiled from Worldometers and The World Bank to analyze how efficient the use of resources were to stabilize the rate of infections and minimize death rates in the top 36 countries that represented 90% of global infections and deaths out of 220 countries as of November 11, 2020. This is the first paper to model the technical efficiency of countries in managing the COVID-19 pandemic by modeling death rates and infection rates as undesirable outputs using the approach developed by You and Yan. We find that the average efficiency of global healthcare systems in managing the pandemic is very low, with only six efficient systems out of a total of 36 under the variable returns to scale assumption. This finding suggests that, holding constant the size of their healthcare systems (because countries cannot alter the size of a healthcare system in the short run), most of the sample countries showed low levels of efficiency during this time of managing the pandemic; instead it is suspected that most countries literally "threw" resources at fighting the pandemic, thereby probably raising inefficiency through wasted resource use.

The Predictive Values of Different Small Vessel Disease Scores on Clinical Outcomes in Mild ICH Patients.

AIM: To explore the predictive values of different small vessel disease (SVD) scores on functional recoveries and the clinical cerebrovascular events in mild intracerebral hemorrhage (ICH). METHODS: In this study, we enrolled conscious and mild ICH patients without surgery and further divided them into the cerebral amyloid angiopathy (CAA)-ICH group and hypertension (HTN)-ICH group. The severity of individual SVD markers, including lacunes, cerebral microbleeds (CMBs), enlarged perivascular spaces (EPVS), white matter hyperintensity (WMH), and cortical superficial siderosis (cSS), was evaluated. The original SVD score, modified SVD score, refined SVD score, and CAA-SVD score and the total number of SVD markers were further calculated. Functional recoveries were evaluated using the modified Rankin scale. Recurrences of stroke were defined as readmission to the hospital with a definite diagnosis of stroke. RESULTS: A total of 163 ICH patients (60 CAA-ICH and 103 HTN-ICH) were included in the study. The CAA-SVD score (OR=3.429; 95% confidence interval (CI)=1.518-7.748) had the best predictive effect on functional dependence in the CAA-ICH group, among which cSS severities probably played a vital role (OR=4.665; 95% CI=1.388-15.679). The total number of SVD markers [hazard ratio (HR)=3.765; 95% CI=1.467-9.663] can better identify stroke recurrences in CAA-ICH. In HTN-ICH, while the total number of SVD markers (HR=2.136; 95% CI=1.218-3.745) also demonstrated association with recurrent stroke, this effect seems to be related with the influence of lacunes (HR=5.064; 95% CI=1.697-15.116). CONCLUSIONS: The CAA-SVD score and the total number of SVD markers might identify mild CAA-ICH patients with poor prognosis. However, it would be better to focus on lacunes rather than on the overall burden of SVD to predict recurrent strokes in HTN-ICH.